digitisation , 2021 promises to be a fruitful – albeit challenging – year for technology . Globally , there are three key predictions that could shape success in the year to come . From cloud to artificial intelligence ( AI ) and digital supply chains , here ’ s what I believe you need to know .

Infor predicts that cloud technology is set to reinvent event experiences in 2021 . After even the US Open tennis tournament successfully pivoted to cloud and AI this year to enhance the virtual experience for fans who could not attend the physical event , we will see an uptick in physical events leveraging cloud technology to give users tailored experiences .

With 2021 primed to grip the world ’ s attention with several major events , such as the Summer Olympics in Tokyo and the Wimbledon Championship , cloud technology is poised to completely reinvent what we even know about fan experiences today . The potential for using cloud technology to transform events is enormous – think real-time crowd excitement analysis to optimise highlights and advertisements , extremely lowlatency live feeds , and moderated crowd interaction – all hosted on robust cloud platforms .

Multi-tenant cloud architectures will become the new gold standard . Using multi-tenant cloud solutions means companies are automatically kept up-to-date with the most cutting-edge technology , without having to worry about manual updates or replacing hardware . As we move into a new year that likely will bring more uncertainty , multi-tenant cloud solutions will become critical technology differentiators , helping businesses remain agile and innovative , while also reducing their e-waste footprints and helping them move closer to their sustainability targets .

Infor ’ s second prediction is that AI will transform the hiring process . In the unpredictable job market of 2021 , it will be critical for organisations to leverage AI to ensure they find the right candidate for the job . AI will enable HR departments to become more proactive in their hiring and help them determine a candidate ’ s cultural fit by using data to measure new terminology such as the quality of a hire .

Innovations such as intelligent screening software that automates resume screening , recruiter chatbots that engage candidates in real-time , and digitised interviews that help assess a candidate ’ s fit will start becoming commonplace in HR departments . AI also holds great promise for creating more diverse and inclusive workplaces ,

given its ability to reduce biases and add objectivity into employment decisionmaking through AI-powered algorithms that will identify the unique qualities of candidates .

As we ’ ve already seen in practice in 2020 , AI will also become mission critical in the healthcare sector , especially as we continue to navigate the extended effects of the coronavirus pandemic . Over the next year , we will also expect to see the accelerated adoption of AI across many more areas of healthcare . By applying machine learning to real-time global data sets , healthcare professionals can more accurately track contact between staff and infected patients , enable accurate diagnoses , utilise predictive analytics to track personal protective equipment ( PPE ), optimise workforce allocations , and develop more effective and lasting vaccinations .

Last but not least , we predict great shifts in traditional supply chains . Supply chains are set to become digital at a rapid pace . Again , as a direct result of Covid-19 , we are going to see the acceleration of digital supply chains in 2021 . While supply chain leaders have traditionally viewed digital transformation in the context of efficiency and cost , the focus will now be on agility and resiliency .

This is where digital technology comes in . A multi-enterprise , digital supply chain enables better end-to-end visibility , better predictive analytics , and better and smarter automation . Leaders will be able to customise and flex their supply chains based on market demand and make better use of ecosystem partners . These digital tools are as far ranging as AI , augmented reality ( AR ), and robotic process automation ( RPA ) and are expected to shift early promises to impactful value propositions .

AI will be critical for effective , real-time supply and demand matching . As the incredible supply chain disruptions of 2020 unfolded and showed us the fragility of a wide range of operations , it became clear that managing real-time supply and demand matching and forecasting were no longer tasks that can be concluded by historical happenings or simple intuition .

It ’ s no longer reasonable to expect a supply chain leader to predict when one country ’ s market will suddenly close and another ’ s will open , or account for evershifting materials and costs – especially as government restrictions on transportation and travel change rapidly . In 2021 , we will see supply chain managers accelerating their adoption of AI to augment workers ’ instincts and experiences and provide them with intelligent insights into changing market conditions , letting them accurately

forecast supply and demand in realtime . This leads to how AI will be useful for manufacturing and processing plants .

AI SUPPLEMENTING PROCESS AND MANUFACTURING PLANT MAINTENANCE

It seems that Charles Darwin ’ s theory of ‘ survival of the fittest ’ applies to any industrial plant management ( especially through what happened in 2020 , and beyond ). Only the smartest enterprises can survive today ’ s aggressive and increasing competition and volatile economic conditions .

Fortunately , modern next-generation analytics are more accessible than ever , giving plant maintenance managers new tools they can leverage to work smarter , not harder .

Driven by AI and ML , augmented analytics provide advanced , prescriptive insights for extending the life of critical assets , analysing overall equipment effectiveness ( OEE ), and preventing unexpected downtime .

Previous generations of analytics focused on using data to produce aesthetically pleasing charts and dashboards . To dive deeper into those reports , managers often had to call in experts . Skilled data scientists and business consultants had to work behindthe-scenes-magic to extract meaningful conclusions from mountains of data .

All too often in these situations the data lost contextual relevance and urgency by the time it passed through the data analyst ’ s filters . This old-school process meant traditional asset maintenance programmes were knee-jerk reactionary and seldom preventive or prescriptive . However , this process is no longer sufficient . Plant managers must upgrade their performance if they want to remain competitive .

Today ’ s advanced analytics can perform the ‘ heavy lifting ’ in the back end and work to connect , prepare and relate data from a variety of seemingly disparate sources across an enterprise . This removes the barriers to entry , giving maintenance teams easy-to-use tools that help to define goals , select algorithms , train the module and test outcomes .

AI ’ s mystery has been replaced by user-empowering interfaces . The results have changed to trusted insights for better business decisions . For industrial plant managers , this means prescriptive asset management and understanding best steps for extending the value of equipment and preventing unplanned downtime that creates gaps in competitiveness .

The use of voice-activated personal digital assistants has also become a reality . Imagine users being able to speak into their

phones and ask , “ How many replacement valves should I order ?” or “ What is the OEE score for this piece of machinery ?”. This represents another new wave of disruption , which some analyst firms , like Gartner , call “ augmented analytics ”.

So , what does augmented analytics mean for plant maintenance ? Solution providers , who specialise in analytics , are setting out to transform the Business Intelligence ( BI ) experience from descriptive ( what is happening ) to diagnostic ( why is it happening ) and predictive ( what will happen ). They are focusing on capabilities which tap into the power of data science to instantly understand the variables driving their Key Performance Indicators ( KPIs ).

Requiring no specialised expertise , such tools help business users automatically find meaningful relationships between a given KPI and countless business variables , and then automatically generate visualisations and dashboards that explain the KPI ’ s behaviour .

For asset maintenance teams , the KPIs could track cost of asset down-time , investment in replacement parts , overall equipment efficiency , and energy consumption . The goal is to monitor and identify early warning signs of a potential asset failure . By spotting the warning signs early , action can be taken to prevent the failure .

More than this , modern analytics will help prescribe the best response . In complex manufacturing , there are often several possible solutions to any asset performance issue .

There are some critical components that must be present when considering a modern augmented analytics solution . These include ; visualisation tools , natural language capabilities , personal digital assistants , contextual relationships , builtin machine learning , and contributing factors ( like the ability to add data about geographic location , environment , weather , suppliers , and product specifications ).

Industrial plant maintenance faces many challenges today , such as pressures to speed response time , meet customer orders , reduce waste and boost productivity . For many organisations , the key lies in boosting performance of the facilities assets .

Improving the lifespan of equipment and making smart decisions about efficiency and repair can make or break an organisation . With advanced augmented analytics now on the table , maintenance has further valuable resource on its side . Now is the time to take the steps to become one of the techsavvy facilities that leverages technology and ensures competitiveness . CLA